Speed of thermal adaptation of terrestrial vegetation alters Earth’s long-term climate

Earth’s long-term climate is driven by the cycling of carbon between geologic reservoirs and the atmosphere-ocean system. Our understanding of carbon-climate regulation remains incomplete, with large discrepancies remaining between biogeochemical model predictions and the geologic record. Here, we evaluate the importance of the continuous biological climate adaptation of vegetation as a regulation mechanism in the geologic carbon cycle since the establishment of forest ecosystems. Using a model, we show that the vegetation’s speed of adaptation to temperature changes through eco-evolutionary processes can strongly influence global rates of organic carbon burial and silicate weathering. Considering a limited thermal adaptation capacity of the vegetation results in a closer balance of reconstructed carbon fluxes into and out of the atmosphere-ocean system, which is a prerequisite to maintain habitable conditions on Earth’s surface on a multimillion-year timescale. We conclude that the long-term carbon-climate system is more sensitive to biological dynamics than previously expected, which may help to explain large shifts in Phanerozoic climate.

weathering potential NPPnorm (eq.8) is a measure of how abiotic environmental conditions (temperature, aridity, radiation), the vegetation's adaptation state, and total productive land area affect the global fluxes of organic and inorganic carbon burial (through fNPP, eq.9).As the model is calibrated to reproduce present-day levels of global organic and inorganic carbon burial, the ratio of NPPnorm at time t to NPPnorm modelled for the present day (t = 0) drives the temporal evolution of the calculated fluxes.As no variability in the state of adaptation occurs in the 'immediate adaptation' scenario, its trajectory represents changes in biotic effects solely due to changes in abiotic environmental conditions.Trajectories for only one of three climatic reconstructions are shown.weathering reaction (10 kJ mol −1 ), weak runoff dependency (kw = 1.5e −6 mm yr −1 ), E) high silicate weathering activation energy (40 kJ mol −1 ) and strong runoff dependency (kw = 1e −3 mm yr −1 ).For more details on the weathering reaction parameters, see West (44) and Maffre et al. (45).F) Reduced maximum enhancement effect of plant productivity on silicate weathering rates (maximum = 4-fold, compared to 10-fold in reference model), G) consideration of a strong CO2 fertilization effect on plant productivity and thus, indirectly on silicate weathering rates; implemented following the GEOCARB model suite (15), H) consideration of a strong CO2 driven weathering feedback in the absence of plants, also following the GEOCARB model (15).Boxes represent the interquartile range, with a line indicating the median.
Whiskers include data points within 1.5 times the interquartile range.

Figure S1 :
Figure S1: Vegetation-weathering relationship.Relationship between catchment scale silicate weathering and A) catchment standing biomass, B) catchment net primary productivity rates.Catchment maps from (45), weathering fluxes from (55), biomass and net primary productivity (NPP) data from CDIAC and NASA EOS data bases, respectively.

Figure S2 :
Figure S2: Input data for CO 2 and predicted temperatures.A) Range of atmospheric CO2 considered, based on reconstruction by Foster et al. (33), B) predicted global average surface temperature (GAST) using the CO2 trajectories and the intermediate complexity climate model PlaSim (34).The obtained temperature curve agrees with the major temperature trends obtained for the Phanerozoic using isotope data (e.g., by Scotese et al. (56)), but differs in the timing and magnitude of fluctuations and temperature extremes.C) Absolute temperature changes between model time steps.

Figure S3 :
Figure S3: Temporal evolution of area-weighted global average NPP norm .The normalized primary productivity and

Figure S4 :
Figure S4: Atmosphere-ocean carbon mass imbalance over the last 390 Myr for different dispersal capacities and speeds of thermal adaptation evolution.A) flora dispersal capacity of 1100 km Myr −1 , B) 1300 km Myr −1 , C) 1500 km Myr −1 , D) 1700 km Myr −1 .The horizontal dashed line depicts the expected carbon mass balance according to the paleothermostat theory.

Figure S6 :
Figure S6: Atmosphere-ocean carbon imbalances reconstructed for two vegetation implementations considering eco-evolutionary adaptation dynamics.The colored area represents the uncertainty in the mass balance for different CO2/climate and solid Earth degassing reconstructions.The dashed line at 0 % depicts the expected mass balance according to the paleothermostat theory.

Figure S7 :
Figure S7: Distribution of flux imbalances over last 390 Myr considering alternative mechanisms that affect the long-term atmosphere-ocean carbon mass balance.A) Reference model, B) linear scaling of land organic carbon burial (Flocb) with global erosion rates as proposed by Hilton (53) (1% increase in erosion = 1% increase in Flocb), C) stronger Flocb-erosion feedback (1% increase in erosion = 4% increase in Flocb), D) different responsiveness of silicate weathering to climate changes (temperature, runoff) as proposed by Penman et al. (54): low activation energy of silicate

Figure S8 :
Figure S8: Sensitivity to penalty parameter k.Effect on A) carbon mass balance and B) terrestrial derived organic matter burial.Default value considered in the study is k =0.25, with larger numbers representing a higher sensitivity of terrestrial floras to temperature changes.Lines indicate a linear fit for each penalty parameter.

Figure S10 :
Figure S10: Climate data and silicate weathering distribution for present day.A) Modelled surface temperatures for pre-industrial CO2 levels using the PlaSim climate model (34), B) normalized aridity index, C) surface net shortwave radiation, D) estimated rates of silicate weathering carbon consumption for an immediately adapting vegetation model.